Geometric separation of partially overlapping nonrigid objects applied to automatic chromosome classification

Gady Agam, Its'hak Dinstein

Research output: Contribution to journalArticlepeer-review

113 Scopus citations

Abstract

A common task in cylogenetic tests is the classification of human chromosomes. Successful separation between touching and overlapping chromosomes in a metaphase image is vital for correct classification. Current systems for automatic chromosome classification are mostly interactive and require human intervention for correct separation between touching and overlapping chromosomes. Since chromosomes are nonrigid objects, special separation methods are required to segregate them. Common methods for separation between touching chromosomes tend to fail where ambiguity or incomplete information are involved, and so are unable to segregate overlapping chromosomes. The proposed approach treats the separation problem as an identification problem, and, in this way, manages to segregate overlapping chromosomes. This approach encompasses low-level knowledge about the objects and uses only extracted information, therefore, it is fast and does not depend on the existence of a separating path. The method described in this paper can be adopted for other applications, where separation between touching and overlapping nonrigid objects is required.

Original languageEnglish
Pages (from-to)1212-1222
Number of pages11
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume19
Issue number11
DOIs
StatePublished - 1 Dec 1997

Keywords

  • Biology computing
  • Chromosome analysis
  • Computational geometry
  • Image segmentation
  • Object recognition
  • Shape decomposition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Geometric separation of partially overlapping nonrigid objects applied to automatic chromosome classification'. Together they form a unique fingerprint.

Cite this